2020
DOI: 10.1038/s42003-020-01125-7
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Improving the diversity of captured full-length isoforms using a normalized single-molecule RNA-sequencing method

Abstract: Human genes form a large variety of isoforms after transcription, encoding distinct transcripts to exert different functions. Single-molecule RNA sequencing facilitates accurate identification of the isoforms by extending nucleotide read length significantly. However, the gene or isoform diversity is lowly represented by the mRNA molecules captured by singlemolecule RNA sequencing. Here, we show that a cDNA normalization procedure before the library preparation for PacBio RS II sequencing captures 3.2-6.0 fold… Show more

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Cited by 9 publications
(11 citation statements)
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“…Despite a clear benefit in expanding the repertoire of gene models, “quantitative” information, such as changes of expression levels, would be lost by cDNA normalization. Differential gene expression results indeed can be more readily possible with data generated from non-normalized Iso-seq libraries but discordant gene expression changes between short- and long-read sequencing platforms have been reported previously [ 68 ]. Indeed, as compared with RNA-seq, the non-normalized Iso-seq dataset prepared from tissues of the same developmental stages gave a skewed representation of abundant transcripts (Addition file 1 : Fig.…”
Section: Discussionmentioning
confidence: 83%
“…Despite a clear benefit in expanding the repertoire of gene models, “quantitative” information, such as changes of expression levels, would be lost by cDNA normalization. Differential gene expression results indeed can be more readily possible with data generated from non-normalized Iso-seq libraries but discordant gene expression changes between short- and long-read sequencing platforms have been reported previously [ 68 ]. Indeed, as compared with RNA-seq, the non-normalized Iso-seq dataset prepared from tissues of the same developmental stages gave a skewed representation of abundant transcripts (Addition file 1 : Fig.…”
Section: Discussionmentioning
confidence: 83%
“…For each type of tissue, more refined algorithms are needed to further ensure that the SEGs from diseased and normal samples are with the same level of expression. While the current study only observed the stable expression of genes at the single-cell level, recent new techniques have been developed that could identify and quantify the expression of single-cell isoforms and even allele-specific isoforms [ 25 , 37 , 38 , 39 ]. Therefore, it would also be interesting to further the single-cell stable expression analysis on transcripts and allele-specific isoforms.…”
Section: Discussionmentioning
confidence: 99%
“…( C ) Expression of a representative spatial-temporal SEG, EIF1 , in single-cell clusters of representative samples. One sample was selected randomly for each of the adult lung, adult stomach, fetal lung and fetal stomach tissues, respectively [ 25 ]. The single cells were clustered according to the gene expression profile, and shown as points in a two-dimension plane of Uniform Manifold Approximation and Projection (UMAP) [ 26 ].…”
Section: Figurementioning
confidence: 99%
“…Due to the identifiability of isoform expression inference ( Hiller et al, 2009 ) and large uncertainty introduced in the process ( Jiang and Wong, 2009 ), isoform-level analysis did not show clear advantages over gene-level analysis in expression changes between the two stages. With the rapid development and wide acceptance of the third-generation long-read sequencing technologies (including PacBio and Nanopore), full-length transcriptome sequencing ( Wang et al, 2019 ; Hu et al, 2020 ) would offer more direct information for isoform-level analysis. Most recently, methods have been developed to sequence poly(A)-tail inclusive full-length transcriptome using the PacBio platform ( Legnini et al, 2019 ; Liu et al, 2019 ), which would be extremely helpful for studying the detailed molecular mechanisms during the dynamic process of oocyte maturation.…”
Section: Discussionmentioning
confidence: 99%